Alvin Roth (2015) Who Gets What - and Why? The New Economics of Matchmaking and Market Design. Houghton Mifflin Harcourt
The Nobel Prize winner Alvin Roth summarizes what are basically his Nobel Prize winning findings in this fascinating book about how markets work and how diligent market design can make them work even better. In 2012 Alvin Roth co-won the Nobel Prize with Lloyd Shapley for their contributions in "the theory of stable allocations and the practice of market design". In other words the theory and practice of solving the coordination problem in assigning kidneys to patients, students to schools, doctors to hospitals. Shapley was responsible for the theoretical contributions back in the 1960s, and Roth was the one behind the actual applications several decades later - designing markets to solve the informational asymmetry problem and the matching problem. (Note: this is the second Nobel prize set that was awarded for matching and search theory. The first was awarded in 2010 to Dale Mortensen, Chris Pissarides, and Peter Diamond).
A few words about matching. As it's name suggests matching is a derivative of the coordination problem, where some people are sellers of a good, and others are buyers, however they cannot usually 'find' each other. In other words, a matching market is one where the price mechanism doesn't usually clear the market. They don't work like regular markets where the price system is successful. On regular markets (think of the stock market or a supermarket) there is no courtship necessary. The sellers don't have to meet the buyers directly nor do they have to engage in any interaction. The price system will make sure both sides are satisfied with the transaction. A matching market on the other hand is a market for human skills not goods or services - labor markets, college admissions, relationships. In none of these does the price system perfectly match supply and demand. Companies usually hire the best workers not the cheapest ones (this depends on the type of job however), as do universities. Both sides of the interaction have to woo each other, signaling their competence on one hand, and their facilities, scholarships/salaries and opportunities on the other. Relationships obviously also depend on courtship. On a date each person is trying to signal their strengths, trying to impress. So whenever a price system doesn't clear the market, economists call this a matching market.
Because of these specific characteristics, matching markets have to be designed a bit differently than regular markets which depend only on prices. Signaling that you want to work somewhere or go to a specific university does not mean you will end up there (or getting the women/man that you want). When there is a lack of kidney donors to make sure every patient gets a transplant, scarcity has to be solved by matching. And the best way of solving the scarcity and coordination problem on the matching market is by clever market design. Every market is based on rules, whether the stock market or the farmers market down the road - all have clearly established mechanisms as to how they operate. Throughout history many of these rules changed and adapted (usually to new technologies), in order for the market to operate more smoothly. So even the spontaneously created markets have their set of rules, and therefore have a specific market design. Roth makes a very convincing case that in order for a market to work well and provide the benefits to society, it requires intelligent and deliberate design. This is hardly a book advocating central planning. Far from it. It lauds markets and simply tries to find ways to fix them when they could perform better; to lower the informational asymmetry; to solve the matching problem; and to set up the very market where exchanges could take place.
So what does it take? In order to work markets need to have a lot of participants - they must be thick. After achieving thickness the next obvious problem that can arise is congestion, making it difficult to select the best alternative (on both sides of the spectrum). When this happens, the market agents can resort to strategic behavior and attempt to trick the system. A well designed market reduces the incentive for doing so. For example in allocating school seats or medical residencies, if the assignment rules are poorly designed participants strategize about expressing their true preferences (their first choice), knowing that they might not get it if it's listed as first choice. In a well designed matching market, this concern is removed and no one has an incentive to behave strategically (hiding their true preferences).
The flavor that the book carries is that economics can be used solve real problems (that's how I saw it). Roth's most famous contribution to that idea, surveyed in chapter 3, is the voluntary kidney exchange he helped set up in the early 2000s, that has so far saved thousands of lives: the New England Program for Kidney Exchange (NEPKE). How does that work? The graphic below explains it:
It's actually quite simple. Someone in your family (your wife e.g.) needs a kidney and you're willing to donate but you're not a match. So what NEPKE does is that it helps you find a matching donor whose kidney is compatible to your wife's, while your kidney is compatible with his. And there you have it - the swap is done and two lives are saved. You didn't have to introduce any new agents. You have the patients, and the altruistic donors. You've solved their asymmetric information problem, avoided the possibility of "gaming the system" or the use of money (which is illegal in kidney transplantation), made the market thick (it attracted a lot of donors and patients), quick (you've managed to avoid congestion), and safe enough for the people to participate. A perfect example of a successful market design. No wonder this won him the Nobel prize.
In addition to kidney exchanges, he mentions a host of other examples of successful market design, all of which he helped to improve: a clearinghouse that matches US medical students to residency programs in hospitals (it's actually a very good example of preference aggregation), assignment of students to nurseries and public schools, Airbnb renting, high-frequency trading, auctions, etc. He even describes the failures and all the potential issues in each matching market he worked on. It's fascinating to read about an economist with so much practical experience in improving every-day life (and even saving lives!). If you're an economist the book will give you this glimmer of hope that all is not lost for our profession, and that economics too can be used to make our lives better.
James Surowiecki (2005) The Wisdom of Crowds. Why the Many Are Smarter Than the Few. Abacus
This book can be summarized in a single sentence: Crowds are smarter than individuals. Controversial, isn't it? To be more specific we can also put it this way: large crowds are smarter (better forecasters, better problem solvers, better innovators) than even the selected elite experts, however this is obviously subject to several conditions. The first thing is for individuals within a group to act independently one from another, meaning that they must reach their decisions/predictions on their own, without any peer pressure or group influence. The second thing is for the group to be diverse in opinion. This means that each person has to have some private info to bring to the group. The more diversity in opinion, the better chance that the given issue can be solved (or precisely predicted). Related to this is decentralization, individuals in the group have to draw their information from specialized, local knowledge. And finally, there has to be a mechanism to aggregate all these individual ideas/thoughts/predictions into a single, collective decision/prediction.
Sounds like a decent theory, there's even some empirical backing to it, given, of course, that all of the aforementioned conditions are satisfied. It's easy to disregard the hypothesis of crowd 'wisdom' calling upon some very basic psychological biases and heuristics we are prone to. Like herding in financial markets (i.e. any type of panic or hype on stock markets), or the availability heuristic and how we tend to rate only the salient information as more important thus disabling us from seeing the 'bigger picture'. Or how we tend to be clueless about statistics and basic statistical inference. Or how we fall victims to anchoring, hindsight bias, illusions of validity, and a host of other things that make us, from a psychological point of view - quite irrational in our values and judgement.
The author doesn't go too deep in uncovering some of the psychological trails of our behavior that make us very bad at predicting things, but somehow, as he claims, our individual shortcomings aren't that important at all. All it matters is that we as a group, collectively, can indeed outperform the experts (which actually isn't too difficult to do, just recall Tetlock's study on the faulties of expert predictions), under the conditions that each person gives his/her prediction independently of the group, that the group is diverse enough and that it benefits from local information. With the proper aggregation mechanism to pull them all together, the predictions should be rather precise. The examples include guessing the weight of an ox, finding a lost submarine in the middle of the ocean, election prediction markets (like the Iowa Electronic Market), the price mechanism, the security community failures, etc.
In all these cases large crowds of diverse individuals, each operating from his/her own specific standpoint are shown to good forecasters. So in a nutshell, our aggregated collective opinion can somehow even out all our individual shortcomings. As if each of our error terms (our variances if you wish) cancel each other out once aggregated. For example, for a single dimension issue have enough people pulling (biased) one way, and enough pulling (biased) the other way, their biases cancel out and we're left with the average which is bound to be true.
Not quite, actually. Statistics doesn't really work that way. Mathematically, the sum of individual variances will only increase the total variance, not decrease it (well, depending on the correlation between individual responses), particularly if we move away from a single-dimension issue (which most of them are). But OK, despite this and some other problems, the argumentation in support of the hypothesis is interesting enough. There is certainly some merit to it, as it has been scientifically tested in multiple occasions and proven to be correct (particularly in forecasting). It's just that the author doesn't really present it this way (which some would call boring), but tries to make an interesting story based on selected examples that support the argument. In a way it's similar to Gladwell's books (both writers actually featured as columnists for the New Yorker) - it's a collection of interesting stories. So just like Gladwell, don't judge it based on its scientific merits (this can take out a lot of fun out of reading), but based on how interesting and enjoyable it is while reading it. After all, it's not that the idea itself doesn't stand. It does, but many conditions must be satisfied. Yet the subtitle: "Why the many are smarter than the few, given that several conditions are satisfied" is not all that fun and won't make you buy the book.